For calculating a position for a robot end effector based on robot joint values, a kinematic model of the robot can be used. For high accuracy positioning of the robot, the kinematic parameters of the robot must be precisely identified. The procedure of performing some motion with the robot, measuring an outcome and fitting a calculated outcome to the measured outcome by adjusting the kinematic parameters is known as robot calibration.
Most commonly an external measurement system is used for calibration. Typically this measurement system is a high precision three dimensional device such as a laser tracker. So called self-calibration methods using constraints have also been proposed. Three dimensional systems are however associated with high costs, and self-calibration systems have limited accuracy as uncertainties from friction will come into play.
One method that has been proposed for robot calibration is to use a laser pointer and a position sensitive device (PSD). However, a problem with laser pointers and PSDs is that they are normally not used in production. Installing them for the sole purpose of calibration would add cost and complexity to the system. For field calibration, i.e. calibration on site after the robot has been installed, a more simple method is needed. Ideally the calibration should be performed without additional sensors or devices, since any external equipment will be associated with cost and complexity.
In the case of a vision guided robot, the camera is part of the system. It would thus be desirable to use the camera to calibrate the robot, in case the robot accuracy needs to be improved for the vision guided hand.
Robot camera calibration has been proposed in literature and tested. By formulating an error function that includes the robot kinematic parameters and the intrinsic and extrinsic camera parameters, the robot and camera parameters can be retrieved by simultaneously optimizing the robot kinematic parameters and camera parameters.
For example, US2013/0274921A1 describes a robot system including a digital camera, where a movable part of the robot has got a mark attached thereto. The digital camera outputs image data by imaging a range of movements of the mark, and a calibrator creates a transformation parameter for correlating a two dimensional coordinate system of the image data with a three-dimensional coordinate system of the movable part.
A drawback with this simultaneous optimization of robot and camera parameters is that a plurality of additional camera parameters must be identified at the same time as the robot parameters. This will make the optimization problem harder and limit the final accuracy of the parameters.
There is thus a need for a simpler and less complex method for identifying the robot parameters.